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        "author": "Chris Spencer",
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            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: General"
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        "description": "Weka - Python wrapper for Weka classifiers\n==========================================\n\n[![](https://img.shields.io/pypi/v/weka.svg)](https://pypi.python.org/pypi/weka) [![Build Status](https://img.shields.io/travis/chrisspen/weka.svg?branch=master)](https://travis-ci.org/chrisspen/weka) [![](https://pyup.io/repos/github/chrisspen/weka/shield.svg)](https://pyup.io/repos/github/chrisspen/weka)\n\nOverview\n--------\n\nProvides a convenient wrapper for calling Weka classifiers from Python.\n\nInstallation\n------------\n\nFirst install the Weka and LibSVM Java libraries. On Debian/Ubuntu this is simply:\n\n    sudo apt-get install weka libsvm-java\n\nThen install the Python package with pip:\n\n    sudo pip install weka\n\nUsage\n-----\n\nTrain and test a Weka classifier by instantiating the Classifier class,\npassing in the name of the classifier you want to use:\n\n    from weka.classifiers import Classifier\n    c = Classifier(name='weka.classifiers.lazy.IBk', ckargs={'-K':1})\n    c.train('training.arff')\n    predictions = c.predict('query.arff')\n\nAlternatively, you can instantiate the classifier by calling its name directly:\n\n    from weka.classifiers import IBk\n    c = IBk(K=1)\n    c.train('training.arff')\n    predictions = c.predict('query.arff')\n\nThe instance contains Weka's serialized model, so the classifier can be easily\npickled and unpickled like any normal Python instance:\n\n    c.save('myclassifier.pkl')\n    c = Classifier.load('myclassifier.pkl')\n    predictions = c.predict('query.arff')\n\nDevelopment\n-----------\n\nTests require the Python development headers to be installed, which you can install on Ubuntu with:\n\n    sudo apt-get install python-dev python3-dev python3.4-dev\n\nTo run unittests across multiple Python versions, install:\n\n    sudo apt-get install python3.4-minimal python3.4-dev python3.5-minimal python3.5-dev\n\nTo run all [tests](http://tox.readthedocs.org/en/latest/):\n\n    export TESTNAME=; tox\n\nTo run tests for a specific environment (e.g. Python 2.7):\n    \n    export TESTNAME=; tox -e py27\n\nTo run a specific test:\n    \n    export TESTNAME=.test_IBk; tox -e py27",
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